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The Discovery of Prognosis Factors Using Association Rule Mining in Acute Myocardial Infarction with ST-Segment Elevation

  • Kwang Sun Ryu
  • Hyun Woo Park
  • Soo Ho Park
  • Ibrahim M. Ishag
  • Jang Hwang Bae
  • Keun Ho RyuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9267)

Abstract

Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.

Keywords

Association rule mining Acute myocardial infarction Prognosis factor 

Notes

Acknowledgments

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2013R1A2A2A01068923) and by the ITRC(Information Technology Research Center) support program (NIPA-2014-H0301-14-1002).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kwang Sun Ryu
    • 1
  • Hyun Woo Park
    • 1
  • Soo Ho Park
    • 1
  • Ibrahim M. Ishag
    • 1
  • Jang Hwang Bae
    • 2
  • Keun Ho Ryu
    • 1
    Email author
  1. 1.Database / Bioinformatics LaboratoryChungbuk National UniversityCheongju-siSouth Korea
  2. 2.Division of Cardiology, Department of Internal MedicineChungbuk National UniversityCheongju cityKorea

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